It turns out that the cost of waiting years for an ISA change is more costly than fixing whatever problems it has.

Waiting a year to get from intuition to theorems is a perfect way to ruin math.

Math is not supposed to be easy/simple, but it supposed to be a great way to understand systems based thinking.

At the same time there could be more examples taught on why these building blocks were historically needed and what they are used for solving nowadays.

LLVM has such a cool tutorial, eventhough I haven't written a programming language myself, it was excitung to go through it to see how static programming languages are lowered to SSA form.

Mojo should have something showing how to write a super fast matmul and especially what's interesting for me is a fast linear attention kernel, as that's where PyTorch is getting much harder to use.

They should show some performance comparisions between PyTorch and Mojo, PyTorch+kernel compilation + Triton vs Mojo, ThunderKittens vs Mojo.

It was always easier than making a system secure.

I still believe it was the good decision, but I also know that I wouldn't be the first person to run the release in prod.

I think Jarred should start making release candidates instead of releases to take some of the pressure off.

Not really as one of the main goals ofr ARC-AGI 3 was measuring task efficiency on unseen games.

I'm sure there are cheats everywhere but the most sensible thing is to just accept that the LLMs of today are much more intelligent in solving reasoning tasks than the ones from half year ago.

My own private benchmark shows the same thing.

Non of these laws stop you from opting out of surveillance, but altogether it gets so hard that at some point you get more suspicious and tracked if you do all this than if you don't do any of these.

Humanity is on aggregate optimizing for increasing collective intelligence and I don't see that stopping as the main goal when AI gets smarter than humanity, just hope that we all will have a nice human life.

I also found Bob Martin entertaining, but after trying to follow his suggestions I just got into the trap that Casey Muratori shows: even if I don't care about the performance of the code at the start, the amount of abstractions Uncle Bob advises makes it just too easy to make the code extremely hard to optimize later.

https://youtu.be/tD5NrevFtbU?is=vuVfjbsINQrtfvbC

If it’s a yes, it needs datacenters and get a lot more energy.

If no, it needs to transfer data to US for training/inferencing on it.

How can they do price fixing and discontinuing a product at the same time? It just looks like some companies are angry that AI / VC industry is outpricing them.

Twitter and Facebook also did what they ,,had to''.

The thing that's new here is that Antropic's growth rate was so enormous that Dario didn't have time to learn to lobby.

It means that most people will never understand how fast the landscape is moving: non-coding and non-homework use cases didn't change that much since last year.